Network pharmacology study of Yishen capsules in the treatment of diabetic nephropathy

Objective In this study, we used network pharmacology to explore the possible therapeutic mechanism underlying the treatment of diabetic nephropathy with Yishen capsules. Methods The active chemical constituents of Yishen capsules were acquired using the Traditional Chinese Medicine Systems Pharmacology platform and the Encyclopedia of Traditional Chinese Medicine. Component target proteins were then searched and screened in the BATMAN database. Target proteins were cross-validated using the Comparative Toxicogenomics Database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target proteins were performed. Then, protein–protein interaction (PPI) analysis was performed using the STRING database. Finally, a pharmacological network was constructed to show the component-target-pathway relationships. Molecular docking was used to analyse the interaction between drug components and target proteins. Results In total, 285 active chemical components were found, including 85 intersection targets against DN. In the pharmacological network, 5 key herbs (A. membranaceus, A. sinensis, E. ferox, A. orientale, and R. rosea) and their corresponding 12 key components (beta-sitosterol, beta-carotene, stigmasterol, alisol B, mairin, quercetin, caffeic acid, 1-monolinolein, kaempferol, jaranol, formononetin, and calycosin) were screened. Furthermore, the 12 key components were related to 24 target protein nodes (e.g., AGT, AKT1, AKT2, BCL2, NFKB1, and SIRT1) and enriched in 24 pathway nodes (such as the NF-kappa B, AGE-RAGE, toll-like receptor, and relaxin signaling pathways). Molecular docking revealed that hydrogen bond was formed between drug components and target proteins. Conclusion In conclusion, the active constituents of Yishen capsules modulate targets or signaling pathways in DN pathogenesis.


Conclusion
In conclusion, the active constituents of Yishen capsules modulate targets or signaling pathways in DN pathogenesis.
life, oral bioavailability, molecular mass, and drug likeness. Yishen capsules was composed of A. membranaceus, A. sinensis, E. ferox, A. orientale, and R. rosea in a ratio of 3:2:3:2:1 [18]. Then, the Encyclopedia of Traditional Chinese Medicine (ETCM) database (http://www. tcmip.cn/ETCM/index.php/Home/Index/index.html) was used to retrieve small drug molecule information that could not be found in the TCMSP database.

Identification of effective components in Yishen capsules
ADME from the TCMSP database was used to screen Yishen capsules for possible small drug molecules. ADME is the study of the body's absorption, distribution, metabolism, and excretion process of exogenous compounds. The parameters used included oral bioavailability, drug likeness, and drug half-life. The components were then screened based on the following thresholds: oral bioavailability �30% and drug likeness �0.18. The ETCM database was used to obtain small molecule information that did not exist in the TCMSP database.

Prediction of Yishen capsule drug targets
After the ADME parameters screening in the last step, the obtained effective components were first converted into PubChem CIDs through the PubChem database (https://pubchem.ncbi. nlm.nih.gov/), and then used as the input items of the Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese medicine (BATMAN) (http://bionet.ncpsb.org/ batman-tcm/). Default parameters were selected. The target protein gene of each component and the corresponding score were calculated, and the component-target protein relationship pair with score � 5 was screened for further analysis.

Cross-validation of small drug molecule target proteins
The Comparative Toxicogenomics Database (CTD) (updated 2018, http://ctdbase.org/) provides information on the associations between chemicals/genes and diseases to help develop disease mechanisms. The phrase "Diabetic, nephropathies" was used as a keyword based on inference scores to identify genes associated with DN in the CTD database. Then, targets scoring >50 were combined with the targets predicted in the previous step. Following logarithmic transformation, inference scores were acquired to assess the functional relationships among targets in the protein-protein interaction (PPI) network.

Pathway enrichment analysis for DN-related targets of Yishen capsules
Using The Database for Annotation, Visualization and Integrated Discovery online bioinformatics resource (Version 6.8, https://david-d.ncifcrf.gov/) [19], KEGG pathway analysis was conducted on genes that were both target proteins of effective components and disease-related genes. The relevant biological processes were selected with p adjusted to �0.05 and counts �2 being considered significant enrichment results. The most significantly associated top 20 signaling pathways were shown using the bar graph.

Protein-protein interaction (PPI) analysis
Using the Metascape online tool (https://metascape.org/gp/index.html#/main/step1), the PPI network of genes that were both target proteins of effective components and disease-related genes were explored.

Construction of pharmacological networks and analysis
To further explore the molecular action of Yishen capsules in DN treatment, herbal medicinecomponent-target proteins-pathway networks were created in Cytoscape. Briefly, drug-effective components, effective components-target protein, and target protein-pathway relationship pairs were input into Cytoscape for network construction. In addition, the module genes identified by the molecular complex detection algorithm in the PPI analysis were selected used to construct a pharmacological network. Nodes of different colors represented the compounds, proteins, or pathways in the pharmacological networks, respectively, and compound-target or target-pathway relationships were presented as edges.

Molecular docking
Four of the target protein nodes in the pharmacological network (AKT1, AKT2, NFKB1, SIRT1) and the small drug molecules beta sitosterol and Stigmasterol targeting them were randomly selected for molecular docking analysis. Information on complexes of target proteins bound to other ligands was downloaded from the Protein Data Bank (PDB) database (http:// www.rcsb.org/) [26] and used for subsequent studies. The criterions for screening conformations included the following: (1) Protein structure obtained by x-ray diffraction method; (2) The resolution of the protein structure is less than three; (3) POLYMER ENTITY TYPE is Protein; (4) Ranked first in descending order of score. Then, pymol (Version 2.0 Schrödinger, LLC.) software was used to remove other ligands and water molecules, and the target protein was isolated for subsequent molecular docking. The molecular structure files of small molecules were downloaded from the PubChem Compound database (https://pubchem.ncbi.nlm. nih.gov/) in SDF format and converted to PDB format by pymol for subsequent molecular docking. Then, based on Lamarckian GA algorithm, Autodock software (Version 4.2.6) [27] was used to study the possibility of molecular docking between small drug molecule and targets.

Composition and component screening
Based on the TCMSP and ETCM databases, the chemical constituents of Yishen capsules were as follows: A. membranaceus (87), E. ferox (26), R. rosea (6), A. sinensis (126), and A. orientale (46). Then, after screening the chemical components of the five medicines through a pre-set threshold, 38 of the original 285 chemical components were finally identified as important chemical components and were used for subsequent analysis (Table 1). Specifically, after screening, A. membranaceus, E. ferox, R. rosea, A. sinensis, and A. orientale were retained with 20, 2, 6, 2, and 8 effective components, respectively.

Prediction, screening, and cross-validation of target proteins
After analysis of BATMAN database, component-target protein pairs were screened according to threshold (scoring �5). The relationship pairs included 13 components and 1283 target proteins. Then, these targets were compared with the results for the 214 genes associated with DN with inference scores of more than 50 in the CTD database. Finally, 85 intersected targets of the 1283 targets and 214 genes were obtained ( Fig 1A).

Molecular docking of target protein and compounds
The results of molecular docking showed that, in beta sitosterol, hydrogen bond was formed between the ligand and ARG-407 of AKT2 and LYS-292 of NFKB1 (Fig 5A and 5B). There was a hydrogen bond between the ligand of beta sitosterol and GLU-496 of SIRT1 (Fig 5C). In addition, hydrogen bond was formed between the ligand of stigmasterol and GLU-200 of AKT2 ( Fig 5D).

Discussion
Diabetic nephropathy increases morbidity and mortality in both type 1 and type 2 diabetes mellitus [28] and is the second most-common cause of chronic kidney disease after chronic glomerular disease [29]. Clinically, microalbuminuria is used as an important index to evaluate DN progression [30]. Hyperglycemia, increased blood pressure, and genetic predisposition are all well-known risk factors of DN [31]. In the present study, following network pharmacology analyses based on PPI targets, a total of 5 key herbs (A. membranaceus, A. sinensis, E. ferox, A. orientale, and R. rosea) and 12 key components (beta-sitosterol, beta-carotene, stigmasterol, alisol B, mairin, quercetin, caffeic acid, 1-monolinolein, kaempferol, jaranol, formononetin, and calycosin) were identified in Yishen capsules. These 12 key components were associated with 24 target protein nodes (e.g., AGT, AKT1, AKT2, BCL2, NFKB1, and SIRT1) and 24 pathways.
In the pharmacological network, AGT, AKT1, AKT2, BCL2, SIRT1, and NFKB1 were important target protein nodes. The production of AGT is involved in DN progression [42]. The T235 AGT polymorphism has been shown to be associated with DN [43]. In addition, a T allele polymorphism in AGT is a genetic risk factor for DN [44]. AKT1 in the renal tubular epithelium and p-Akt1 (Ser(473)) are more prevalent in diabetic patients [45]. AKT2 silencing prevents renal protection in mice with streptozotocin-induced diabetes [46]. Diabetic patients with poor glycemic control exhibit the downregulation of BCL2, which activates the NF-kB pathway, thus leading to the development of nephropathy [47]. The regulation of beclin1/ UVRAG/BCL2 could be involved in the cell apoptosis and cell autophagy observed in DN [48]. Therefore, AGT, AKT1, AKT2, and BCL2 may be crucial proteins in the action of Yishen capsules against DN.
SIRT 1 regulates the Bax and Bcl-2 apoptotic proteins in DN [49], and a NFKB1 gene polymorphism (rs28362491) is associated with DN [50]. NFKB1 variations contribute to the development of type 2 diabetes mellitus [51], and the expression of NFκB is upregulated in diabetic patients [52]. SIRT1 and NFKB were both present in the pharmacological network constructed in the present study. Additionally, the NF-kappa B signaling pathway was found to play a key role in DN in the pharmacological network. Previous studies have indicated that the NFκB signaling pathways are involved in the development mechanisms of DN [53,54]. Our previous study confirmed that Yishen capsule promotes podocyte autophagy through regulating SIRT1/ NF-κB signaling pathway to improve diabetic nephropathy [18].
Moreover, in total, 20 pathway nodes were found to be associated with DN in the target network of cross-validation targets. The AMPK signaling pathway improves DN by reducing uric acid, serum albumin, creatinine, and kidney damage [55]. The AMPK signaling pathway has been shown to alter fatty acid oxidation and glucose in C57BL/6 mice with type 2 diabetes [56]. The p38 MAPK signaling pathway plays an important role in modulating cell differentiation, growth, and death [57]. Elevated mRNAs in the PKC-MAPK pathway are essential in the glomerular lesion damage observed in DN [58]. Moreover, the Ras signaling pathway has been demonstrated to affect streptozotocin/nicotinamide mice [59] or diabetes-induced VEGFmediated nephropathy [60]. Thus, the AMPK and Ras signaling pathways influence the development of DN.

Conclusion
In conclusion, beta-sitosterol, stigmasterol, quercetin, caffeic acid, and kaempferol were identified as key components of Yishen capsules in the treatment of DN. AGT, AKT1, AKT2, and BCL2 may be important target proteins in the pharmacological network. Moreover, SIRT1 and NFKB1 may interact to regulate DN via the NF-kappa B signaling pathway. Furthermore, the AMPK and Ras signaling pathways are highly important in DN. However, more research is required to further validate these results.